PROJECT SUMMARY/ABSTRACT Gonorrhea is the second most common notifiable disease in the United States with 555,608 cases reported in 2017. In recognition of the high prevalence of gonorrhea and increasing antibiotic resistance, in 2013, the U.S. Centers for Disease Control and Prevention named antimicrobial-resistant (AMR) gonorrhea among the three most urgent infection threats in the United States. The continued evolution of AMR gonorrhea highlights the importance of surveillance systems that provide timely and accurate data to inform public health strategies to combat the spread of AMR gonorrhea. In this context, important questions remain on how best to configure a surveillance system: Where should the surveillance sites be located? How many isolates should be tested for drug susceptibility among risk groups, such as men who have sex with men, men who have sex with women, and women? How should sampling be distributed among the anatomical sites of infection (urethra, rectum, and/or pharynx)? This proposal describes a rigorous, simulation model-based investigation to evaluate strategies for the surveillance of AMR gonorrhea in the United States. Toward this goal, we will develop a simulation model of gonorrhea transmission in the 50 most populous metropolitan areas in the United States. The model will allow us to enumerate several performance measures including: 1) the clinically-effective lifespan of antibiotics, 2) the incidence of drug-susceptible and AMR gonorrhea, and 3) the overall cost of surveillance, diagnosis, and treatment of gonorrhea. We will use this model to project the impact of various strategies for the surveillance of AMR gonorrhea on these performance measures. This study will provide essential information to help policy makers identify strategies for the surveillance of AMR gonorrhea that are both cost-effective and expected to extend the clinically-effective lifespan of antibiotics. To ensure the successful completion of these aims, we have assembled a team of experts in gonococcal antimicrobial resistance, mathematical and computer simulation modeling, decision science, and health economics who are committed to working together to slow the spread of AMR gonorrhea in the United States by identifying strategies to optimize the surveillance of AMR gonorrhea.